paraqeet.model.equation_of_motion.EquationOfMotion#

class paraqeet.model.equation_of_motion.EquationOfMotion(hamiltonian)#

Bases: Optimizable

Represents the equation of motion for a given Hamiltonian.

Implementations can for example be the Schrödinger equation for a closed system, Lindbladian for an open system, or Hamilton’s equations for a classical system.

Parameters:: hamiltonian (Hamiltonian) – Matrix representation of a Hamiltonian.

__init__(hamiltonian)#

Parameters:: hamiltonian (DifferentiableHamiltonian)

Methods

`__init__`(hamiltonian)
`get_parameters`()	Return all parameters of this class that can be optimized.
`get_right_hand_side`(time, state)	Return the right-hand side of the equations of motion.
`get_value`(times)	Abstract method to get the prefactor matrix.
`get_value_and_gradient`(times)	Implement the gradient of either getEquationOfMotion or getMatrixEOM.
`set_all_optimizable_parameters`(all_params)	Set all optimizable parameters in the optimization.
`set_optimizable_parameters`(params)	Set which parameters associated with the object shall be considered during optimization.

Attributes

`all_optimizable_parameters`	Get the optimizable parameters
`name`	Get the name of the parameter.
`ode_propagation`
`optimizable_parameters`	Get the optimizable parameters

property all_optimizable_parameters: list[Quantity]#

Get the optimizable parameters

Returns:: The list of all the optimizable parameters considered in the optimization
Return type:: list[Quantity]

abstract get_parameters()#

Return all parameters of this class that can be optimized.

Raises:: NotImplementedError – Subclasses derived from this class must implement this method.
Return type:: list[Quantity]

get_right_hand_side(time, state)#

Return the right-hand side of the equations of motion.

The format depends on the implementation and could for example be a state vector or a matrix. Default implementation assumes a homogeneous ODE with matrix operator given by self.getMatrix().

Parameters:

time (Array) – Any one-dimensional vector of timestamps.
state (Array)

Returns:

The right-hand side of the equation of motion at each time stamp.

Return type:

Array

abstract get_value(times)#

Abstract method to get the prefactor matrix.

Parameters:: times (Array) – Any one-dimensional vector of timestamps.
Returns:: Returns matrix equations of motion.
Return type:: Array
Raises:: NotImplementedError – Subclasses derived from this class must implement this method.

abstract get_value_and_gradient(times)#

Implement the gradient of either getEquationOfMotion or getMatrixEOM.

Parameters:: times (Array) – Any one-dimensional vector of timestamps.
Raises:: NotImplementedError – Subclasses derived from this class must implement this method.
Return type:: tuple[Array, Array]

property name: str | None#

Get the name of the parameter.

Returns:: Name of the parameter.
Return type:: str | None

property optimizable_parameters: list[Quantity]#

Get the optimizable parameters

Returns:: The list of optimizable parameters associated with the object.
Return type:: list[Quantity]

set_all_optimizable_parameters(all_params)#

Set all optimizable parameters in the optimization.

Parameters:

params (List[Quantity]) – List of optimizable parameters to be set.
all_params (list[Quantity])

Return type:

None

set_optimizable_parameters(params)#

Set which parameters associated with the object shall be considered during optimization.

All quantities that are not in the response of get_parameters will be filtered out. This function is called by the optimizer before gradient based optimization to tell the layers which gradients to compute.

Parameters:: params (list[Quantity]) – List of optimizable parameters to be set.
Return type:: None